def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = channel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func=cifar10_inception_v3_wd, optimizer=tf.train.RMSPropOptimizer, optimizer_args={ 'momentum': 0.9, 'decay': 0.9, 'epsilon': 1.0 }, n_epochs=100, batch_size=32, aux_loss_weight=0.3, label_smoothing=0.1, lr_decay_func=ExponentialDecayValue(start=0.045, decay_rate=0.94, decay_steps=2), weight_decay=0.00004) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = global_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func=cifar10_sequential_cbn6d_wd, optimizer=tf.train.RMSPropOptimizer, optimizer_args={ 'decay': 0.9, 'epsilon': 1e-8 }, n_epochs=125, batch_size=64, lr_decay_func=DivideAtRatesWithDecay(start=0.001, divide_by=2, at=[0.6, 0.8], max_steps=125, decay=1e-6), weight_decay=0.0001) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = global_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func = partial(cifar10_sequential_clrn5d3_wd, drop_rate = 0.3), optimizer = tf.train.AdamOptimizer, optimizer_args = None, n_epochs = 100, batch_size = 128, lr_decay_func = FixValue(value = 1e-4), weight_decay = 0.0001 ) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = channel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func=cifar10_bn_inception_v1_wd, optimizer=tf.train.MomentumOptimizer, optimizer_args={'momentum': 0.9}, n_epochs=250, batch_size=128, lr_decay_func=ExponentialDecay(start=0.01, stop=0.0001, max_steps=200), weight_decay=0.00004) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = global_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func=partial(cifar10_sequential_c3d_selu_drop, drop_rate=0.5), n_epochs=50, batch_size=128, lr_decay_func=ExponentialDecay(start=0.001, stop=0.0001, max_steps=50), optimizer=tf.train.AdamOptimizer, weight_decay=None, augmentation=False) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = pixel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func = cifar10_mobilenet_v2_wd, optimizer = tf.train.RMSPropOptimizer, optimizer_args = {'decay': 0.9, 'momentum': 0.9}, n_epochs = 300, batch_size = 96, lr_decay_func = DecayValue( start = 0.045, decay_rate = 0.98), weight_decay = 0.00004 ) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = pixel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func=cifar10_shufflenet_wd, optimizer=tf.train.MomentumOptimizer, optimizer_args={'momentum': 0.9}, n_epochs=300, batch_size=128, lr_decay_func=LinearDecay(start=0.5, stop=0.0, max_steps=300), weight_decay=4e-5) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = pixel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func = cifar10_sequential_allconvC_wd, optimizer = tf.train.MomentumOptimizer, optimizer_args = {'momentum': 0.9}, n_epochs = 350, batch_size = 64, lr_decay_func = DivideAt( start = 0.05, divide_by = 10, at_steps = [200, 250, 300]), weight_decay = 0.001 ) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = pixel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func = cifar10_resnet_bottleneck_20_wd, optimizer = tf.train.MomentumOptimizer, optimizer_args = {'momentum': 0.9}, n_epochs = 200, batch_size = 128, lr_decay_func = DivideAtRates( start = 0.1, divide_by = 10, at = [0.5, 0.75], max_steps = 200), weight_decay = 0.0001 ) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = pixel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func = partial(cifar10_resnext_29_wd, cardinality = 8, group_width = 16), optimizer = tf.train.MomentumOptimizer, optimizer_args = {'momentum': 0.9}, n_epochs = 300, batch_size = 128, lr_decay_func = DivideAtRates( start = 0.1, divide_by = 10, at = [0.5, 0.75], max_steps = 300), weight_decay = 0.0005 ) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = channel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func = partial(cifar10_densenet_40_wd, drop_rate = 0.0), optimizer = tf.train.MomentumOptimizer, optimizer_args = {'momentum': 0.9}, n_epochs = 300, batch_size = 64, lr_decay_func = DivideAtRates( start = 0.1, divide_by = 10, at = [0.5, 0.75], max_steps = 300), weight_decay = 0.0001 ) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = channel_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func=partial(cifar10_xception_wd, drop_rate=0.5), optimizer=tf.train.MomentumOptimizer, optimizer_args={'momentum': 0.9}, n_epochs=100, batch_size=32, lr_decay_func=ExponentialDecayValue(start=0.045, decay_rate=0.94, decay_steps=2), weight_decay=0.00001) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)
def main(): tr_x, tr_y, te_x, te_y = load_cifar10_data() tr_x, te_x = global_mean_std(tr_x, te_x) mean_acc, max_acc, min_acc = eval_net_custom( tr_x, tr_y, te_x, te_y, net_func=cifar10_inception_v3, optimizer=tf.train.AdamOptimizer, optimizer_args=None, n_epochs=50, batch_size=128, aux_loss_weight=0.3, label_smoothing=0.1, lr_decay_func=ExponentialDecay(start=0.01, stop=0.001, max_steps=50), weight_decay=None, augmentation=False) print("Mean accuracy: ", mean_acc) print("Max accuracy: ", max_acc) print("Min accuracy: ", min_acc)